One of the key service quality issues of any wireless network is providing complete and reliable radio frequency (RF) coverage. This is accomplished by minimizing the number of RF holes in the coverage area of the wireless network. An RF hole is an area in which the forward channel signals transmitted by the base station or the reverse channel signals from the mobile station are severely degraded by terrain, buildings, vegetation, or any other object. When a wireless device (or mobile station) that is turned on, but idle, moves into an RF hole, service may be lost (i.e., the mobile station is dropped) because the mobile station cannot receive the pilot channel signal, the paging channel signal, the synchronization channel signal, or other control channel signal. The mobile station must reacquire the wireless network when the mobile station moves out of the RF hole. If the mobile station is on and active (i.e., handling a voice call or an Internet protocol (IP) data call), the mobile station may drop the call because the forward or reverse traffic channel signal is blocked.
Wireless services providers routinely monitor their wireless networks in order to detect RF holes. Conventional methods of improving RF coverage have relied on costly forms of maintenance. Wireless service providers rely on pre-commercial optimization of the system using test equipment and test drives and/or post-commercial optimization using test drives and regularly scheduled visits to the equipment sites. Service providers also use alarms and sensors within the fixed infrastructure equipment and reports generated by diagnostic software within the fixed infrastructure. Service providers also rely on complaints from subscribers about degradation of service. However, all of these methods have significant drawbacks.
Pre-commercial optimization of the system using test equipment and test drives occurs when the system is not loaded, so the system is optimized for conditions that practically never exist in normal operation. Such tests are static and cannot possibly account for different loading conditions. Also, pre-commercial optimization is often implemented on a sample area of the network and it is assumed that the rest of the system behaves similarly. This method is highly inaccurate and expensive.
Post-commercial optimization in an active system is very slow and costly. Post-commercial optimization is done with a limited number of mobiles and without a system-wide point-of-view. Traffic loads and patterns change rapidly, so that by the time the information is collected it may no longer be valid. Moreover, post-commercial optimization must be done with very small impact on service to subscribers. This limits the effectiveness of the process. Regularly scheduled maintenance visits to the equipment sites are very expensive and very slow.
The effectiveness of alarms and sensors in wireless network infrastructure equipment is limited by the exceeding complexity of many wireless networks. Monitoring only failures in the infrastructure is insufficient, since the alarms and sensors cannot measure the actual experience of the subscriber under normal operational conditions. Finally, waiting for customers to complain is not a good way to do business. Moreover, customer complaints are sporadic and seldom contain the detailed information needed to detect and correct a network coverage problem.
More recently, advanced mobile location services (e.g., mobile E911) have been developed to accurately determine the location of mobile stations (e.g., cell phones). Advantageously, service providers may use these location services to identify RF coverage holes. These location services generally fall into one of two categories: 1) handset-based techniques or 2) network-based techniques. Network-based techniques use mobile station locating equipment that is disposed in the network infrastructure (e.g., in a base station). Handset-based techniques use locating equipment (e.g., GPS receiver) that is disposed in the handset and transmit position data to the network.
However, unless the wireless service provider continuously monitors the location of all mobile stations, it is difficult to use that information to determine coverage holes. Continually monitoring the positions of all mobile stations also consumes an excessive amount of RF bandwidth and processing power in the base stations. Furthermore, network-based location devices determine mobile station location using principles of extrapolation (e.g., triangulation) that often prove inaccurate.
Finally, none of the handset-based or network-based techniques for monitoring the RF coverage of a wireless network are able to control for faults in the mobile station itself, rather than in the network. Mobile stations are often dropped even in areas of good RF coverage because the mobile station is defective, although still able to operate. When these mobile stations are dropped, it may lead to false reports of poor RF coverage in the wireless network.
Therefore, there is a need in the art for improved systems and methods for automatically optimizing the RF coverage in a wireless network. In particular, there is a need in the art for improved systems and methods for automatically identifying and correcting RF coverage holes in a wireless network. More particularly, there is a need in the art for RF coverage monitoring apparatuses and techniques that are capable of identifying faulty mobile stations that may generate false reports of RF coverage holes in a wireless network.